A challenging project for challenging times

The joint project EDE 4.0 (short for Extended Dynamic Felling Planning), which is funded by the German Federal Ministry of Food and Agriculture (BMEL), hits a sore spot in forest management. This is because, in addition to the serious effects of climate change, the forestry industry has been increasingly struggling with challenges in its day-to-day operations for several years now. These range from growing precincts per capita to rising retirement rates to contrasting demands that stakeholders such as investors, citizens, and climate activists are placing on forests and their management. All of this is important for forest employees to consider in decision-making processes. "If district foresters are assigned additional areas, as is currently increasingly common, or if they are new to their job, they still need to be able to make decisions quickly for their district. This is where our application can help provide an overview," explains Dr. Thomas Freudenmann, CEO of EDI GmbH.

The cloud-based application, developed with the EDI hive IoT framework, supports forest management by using artificial intelligence (AI) and formalized expert knowledge to decide where to harvest or when to replant, including the likelihood of success of a planting at a defined location. In doing so, the intelligent assistance system provides forestry employees with individual, data-based risk assessments of the current situation as well as editable, alternative scenarios - if required, also directly in the field. To this end, the system draws on the expertise of the South German Climate Office, which evaluates climate forecasts from the BMBF MiKlip project for several months and years in advance, as well as on forestry knowledge from the IfGG, such as tree growth rates. The expert knowledge of forestry employees is also incorporated. Dr. Hans Schipper, head of the South German Climate Office at KIT, explains: "Our system in the project is basically modular: The advantage of this is the flexibility to add further modules at any time as needed, to change existing ones, or to increase the complexity." Finally, the algorithm processes all the information fed in. It converts the information into known performance indicators so that visually simple and intuitive decision recommendations can be made in the application for measures in the defined areas. The focus here is on transparency and clarity, as Dr. Ana Cordeiro Pires from the South German Climate Office emphasizes: "We're not talking  about information such as specific temperature values or rainfall amounts, but whether it will be warmer or colder and how likely certain events are. With this information in mind, forestry workers can better decide whether they want to take a risk." In this way, those involved in the project want to ensure that users receive targeted recommendations at all times without being overwhelmed by the amount of information or the level of detail.